Applied ML Engineer

Paramount•Los Angeles, CA

3d•Remote

About The Position

The Applied ML Engineer builds applied machine learning systems within a Production Platform Engineering pod. This role translates technical direction into working software, including model integrations, data pipelines, retrieval systems, evaluation instrumentation, and service layers. Working within defined execution cycles, the Applied ML Engineer delivers modular, testable systems that can be evaluated, integrated, and extended by downstream teams. The role focuses on implementation and iteration, not architecture ownership, model behavior definition, or system recovery. Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment™. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME®.

Requirements

4+ years of software engineering experience in backend, systems, or ML-adjacent environments.
2+ years hands-on ML implementation.
Proficient programming skills, particularly in Python, and experience building APIs or services.
Working knowledge of machine learning systems, including model integration, evaluation, and debugging.
Experience building end-to-end systems from unclear requirements to working software.
Ability to operate in dynamic, iterative development environments.

Nice To Haves

Experience building ML-enabled systems such as retrieval pipelines, agent workflows, or model-backed services.
Familiarity with evaluation instrumentation, logging, and tracing in ML systems.
Experience working with cloud-based infrastructure and distributed systems.
Contributions to reusable libraries, frameworks, or internal platforms.

Responsibilities

Implement machine learning systems, including model integrations, data pipelines, retrieval systems, evaluation instrumentation, and service layers.
Translate technical direction into modular, maintainable codebases with clear interfaces.
Deliver working artifacts at defined milestones, including code, configuration, tests, and documentation.
Iterate on systems based on evaluation results, domain feedback, and integration requirements.
Refine performance, usability, and functionality within the scope of the system being built.
Support rapid development cycles while maintaining code quality and reproducibility.
Implement evaluation hooks, metrics, and instrumentation defined by the ML Behavior Systems team.
Ensure systems can be tested against defined benchmarks and quality standards.
Support debugging and iteration based on evaluation outcomes.
Build systems using ML Platform & Operations infrastructure for training, inference, and deployment.
Ensure compatibility with platform services, APIs, and constraints.
Follow established patterns for system integration and deployment readiness.
Design outputs as modular components with stable interfaces.
Include configuration controls, observability hooks, and error handling required for integration.
Partner with Platform Integration teams to ensure deliverables meet downstream requirements.